Conventionally, photographs have been printed by analog exposure whereby light is projected onto a photographic film recording having an original image thereon, so that the light having passed through that photographic film illuminates photographic printing paper. Another popular method in recent years is digital exposure whereby monochromatic red, green, and blue lights are projected on each pixel on photographic printing paper for printing on the basis of digital image data obtained by scanning an image on a photographic film with a scanner or the like or by taking photographs with a digital camera, etc.
In a photographic printing device for carrying out digital exposure, the pixel density of an image printed on photographic printing paper is dictated by the resolution of the scanner and that of the exposure head. Particles forming an image on a positive film have a typical density of about 2500 dpi. Digital-exposure photographic printing devices are already introduced to the market with the same level of resolution as the particle density. The digital-exposure photographic printing device is capable of acquiring an image having the same level of resolution as the particle density of a film and printing on photographic printing paper images that are not inferior to those printed by analog exposure.
Note that the particle density of 2500 dpi is equivalent to 3445×2362 pixels on a 135 F-size (3.6 cm×2.4 cm) photographic film.
The photographic printing device for carrying out digital exposure can process image data and produces on the image such various special effects that an analog-exposure photographic printing device cannot produce. One of the special effects is sharpening whereby images (for example, those of people in the background and of facial features of a person) have prominent edges. In the following description, more details will be given as to sharpening.
Sharpening is a process to form clear boundaries, i.e., edges, between adjacent objects in an image. Specifically, a set of data, termed a spatial filter, is used on image data to convert the image data in such a manner to impart large luminosity differences to target pixels constituting an edge and their adjacent pixels. A very simple example is given below.
A 3×3 spatial filter is expressed as a 3×3 array, for example,                        0                              -          1                            0                                      -          1                            5                              -          1                                    0                              -          1                            0            where each element represents a coefficient applied to the luminosity of one of the 3×3 pixels. Here, the middle value, 5, is the element applied to the target pixel, and the other values are those applied to the adjacent pixels of the target pixel. The 3×3 filter is basically devised so that its elements add up to 1.
For example, a 100×100 pixel image is subjected to the 3×3 filter 10000 times, with a different pixel chosen as the target pixel each time, to sharpen the whole image.
Effects of the 3×3 filter will be described by way of a concrete example. An image of a car, an airplane, or another object on a road, the sky, or another background as has many, what we call, monotonous parts where the image varies little in chromaticity and luminosity in the background. As an example, the luminosities of 3×3 pixels in a monotonous part of the image are represented by a 3×3 array,                        51                    49                    53                            52                    50                    49                            48                    51                    47            
Multiplying the elements of the 3×3 filter array and the associated elements of the 3×3 pixel array, element by element, is equivalent to applying the filter to the pixels. The filtered luminosities of the pixels are represented by a 3×3 array,                        0                              -          49                            0                                      -          52                            250                              -          49                                    0                              -          51                            0            Since the values of the filtered luminosity add up to 49, the value, 50, of the target pixel is now set to 49. It would be understood from this example that the filtering hardly changes luminosity in the monotonous part of the image.
The luminosities of 3×3 pixels on an edge of the object are represented by a 3×3 array, for example,                        10                    30                    70                            15                    50                    90                            20                    80                    85            At this part of the edge, values are low near the upper left corner and high near the lower right corner. Applying the 3×3 filter to the 3×3 pixels, we obtain                        0                              -          30                            0                                      -          15                            250                              -          90                                    0                              -          80                            0            Since the values of the filtered luminosity add up to 35, the value, 50, of the target pixel is now set to 35.
Now moving to a new target pixel with a value, 90, which is to the right of the target pixel, the luminosities of the 3×3 pixels are represented by a 3×3 array, for example,                        30                    70                    85                            50                    90                    95                            80                    85                    90            Applying the 3×3 filter to the 3×3 pixels, we obtain                        0                              -          70                            0                                      -          50                            450                              -          95                                    0                              -          85                            0            Since the values of the filtered luminosity add up to 150, the value, 90, of the target pixel is now set to 150. It would be understood from this example that the filtering changes luminosity by great amounts in the edge part of the image.
FIGS. 11(a), 11(b) illustrate the image data at the edge part before and after the filtering. The illustration tells that sharpening is a process to add a spike-like amplification in FIG. 11(b) to the original contrast in FIG. 11(a) at an edge to enhance the contrast at the edge.
To sum up the description so far, the spatial filter, when used in processing image data, hardly changes the image data in monotonous parts of the image, but increases luminosity differences at the edges. Subjecting all the pixels of the image to this process enhances the sharpness of the whole image.
This conventional sharpening method, however, has a problem; images printed on photographic printing paper will have their coarse look exacerbated too.
The aforementioned high resolution digital-exposure photographic printing device is capable of acquiring an image having practically the same level of resolution as the particle density of a film. A photograph, if printed on the same scale as the photographic film, is made of pixels, each as large as a film particle. The film particles share among them very similar, but not completely identical, coloring properties and cause fine variations in chromaticity and luminosity. The “noise” occurring in the film particle level (hereinafter, will be referred to as film particle noises) is passed on in the course of printing, causing a coarse look of the printed photograph.
The greater the proportion relative to the image acquired from a photographic film is by which a photograph is scaled up in the course of projection and printing on photographic printing paper, the more distinct the film particle noise of the resultant photograph appears.
In short, the conventional sharpening method exacerbates the particulate nature of the photographic film, as well as enhances edges in an image, imparting a more coarse look to the image printed on photographic printing paper. The resultant image may look very ugly. Image quality degrades, especially, if human skin gives a rough look.
The following will describe sharpening as a cause of the exacerbation of film particle noise by way of a concrete example. An example of 3×3 pixels is given having luminosities represented by a 3×3 array,                        45                    45                    45                                                 45                            90                    45                            45                    45                    45            where the middle value, 90, is film particle noise.
Applying the 3×3 filter to the 3×3 pixels, we obtain                        0                              -          45                            0                                      -          45                            450                              -          45                                    0                              -          45                            0            Since the values of the filtered luminosity add up to 270, the value, 90, of the target pixel is now set to 270. It would be understood from this example that the filtering exacerbates the noise by a fairly great amount.
To address this problem, we need a process whereby sharpening can be carried out without accompanying exacerbation of a coarse look, which could be otherwise caused by film particles. A simple method to implement this is blurring, that is, a repeated process of replacing a value of a target pixel with a mean value of its surrounding pixels until the process covers the whole image. The blurring process, however, goes too far and blurs edges which are exactly where we wanted to increase sharpness in the first place.
A possible alternative is to implement a blurring process on acquired image data before sharpening. In this case, however, the image will be stripped of its minute details.